让大模型在复杂图中推断因果关系中的混杂变量。
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
- 基于部分因果图生成完整结构，需结合上下文推理
- 4000+任务测试显示大模型能有效识别混杂因子
- 适合从事因果推断与AI辅助科研的研究者
科学发现推动人类认知进步，其核心是因果推断，揭示现象背后的机制。尽管随机实验提供强因果推断，但常因伦理或实践限制难以实施。观测研究则易受混杂或中介偏差影响。识别这些后门路径成本高，依赖科学家的领域知识生成假设。本文提出一个新基准，目标是补全部分因果图。该基准包含超过4000个不同难度的查询。实验表明，大语言模型（LLMs）具备推断原因与结果之间混杂变量的强大能力。不同于对固定关联的记忆，本任务要求模型根据整个图的上下文进行推理。
原文摘要 · Abstract (English)
Scientific discovery catalyzes human intellectual advances, driven by the cycle of hypothesis generation, experimental design, evaluation, and assumption refinement. Central to this process is causal inference, uncovering the mechanisms behind observed phenomena. While randomized experiments provide strong inferences, they are often infeasible due to ethical or practical constraints. However, observational studies are prone to confounding or mediating biases. While crucial, identifying such backdoor paths is expensive and heavily depends on scientists' domain knowledge to generate hypotheses. We introduce a novel benchmark where the objective is to complete a partial causal graph. We design a benchmark with varying difficulty levels with over 4000 queries. We show the strong ability of LLMs to hypothesize the backdoor variables between a cause and its effect. Unlike simple knowledge memorization of fixed associations, our task requires the LLM to reason according to the context of the entire graph.
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